Prefill of raid stripes in a storage system by reading of existing data

ABSTRACT

A storage system comprises a plurality of storage nodes each comprising one or more storage devices. At least a given one of the storage nodes is configured to read data blocks from its one or more storage devices, and for a given one of the data blocks, to determine based at least in part on a content-based signature of that data block whether or not the given data block is appropriate for use in a prefilling operation of the given storage node. Responsive to the given data block being appropriate for use in the prefilling operation of the given storage node, the given storage node uses the data block in the prefilling operation of the given storage node. Responsive to the given data block not being appropriate for use in the prefilling operation of the given storage node, the given storage node sends the data block to another one of the storage nodes for use in a prefilling operation of that other storage node.

RELATED APPLICATION(S)

The present application claims priority to U.S. Provisional patentapplication Ser. No. 62/734,671, filed Sep. 21, 2018 and entitled“Distributed Generation of Random Data in a Storage System,” which isincorporated by reference herein in its entirety. The presentapplication is also related to U.S. patent application Ser. No.16/446,183, filed concurrently herewith and entitled “DistributedGeneration of Random Data in a Storage System,” and U.S. patentapplication Ser. No. 16/446,161, filed concurrently herewith andentitled “Automatic Prefill of a Storage System with Conditioning ofRAID Stripes,” each also claiming priority to the above-cited U.S.Provisional application Ser. No. 62/734,671, and incorporated byreference herein in its entirety.

FIELD

The field relates generally to information processing systems, and moreparticularly to storage in information processing systems.

BACKGROUND

In many storage systems, data is distributed across multiple storagedevices in accordance with redundant array of independent disks (RAID)arrangements. Some RAID arrangements allow a certain amount of lost datato be rebuilt from parity information, typically in response to astorage device failure or other type of failure within the storagesystem. It is desirable in certain contexts to be able to “prefill” atleast a portion of a RAID array with random data. For example, suchprefilling is desirable prior to performing various performance tests atregular intervals, including while varying the hardware or software usedon that RAID array. Due to the nature of a RAID array, its performancecan be drastically different based on how full the array is and whetheror not the individual blocks have been used previously. Unfortunately,conventional techniques for prefilling the RAID array with suitableamounts and types of data are inefficient and unduly time consuming.

SUMMARY

Illustrative embodiments provide techniques for prefill of RAID stripesin a storage array or other type of storage system by reading ofexisting data. For example, such embodiments can avoid the drawbacks ofconventional approaches, providing highly efficient mechanisms forprefilling of a RAID array with appropriate amounts and types of data.As a result, performance testing is more accurate and can be performedmore quickly than would otherwise be possible using the conventionalapproaches.

In one embodiment, a storage system comprises a plurality of storagenodes each comprising one or more storage devices. Each of the storagenodes further comprises a processor coupled to a memory. At least agiven one of the storage nodes is configured to read data blocks fromits one or more storage devices, and for a given one of the data blocks,to determine based at least in part on a content-based signature of thatdata block whether or not the given data block is appropriate for use ina prefilling operation of the given storage node. Responsive to thegiven data block being appropriate for use in the prefilling operationof the given storage node, the given storage node uses the data block inthe prefilling operation of the given storage node. Responsive to thegiven data block not being appropriate for use in the prefillingoperation of the given storage node, the given storage node sends thedata block to another one of the storage nodes for use in a prefillingoperation of that other storage node.

These and other illustrative embodiments include, without limitation,apparatus, systems, methods and processor-readable storage media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one example of an information processingsystem within which one or more illustrative embodiments areimplemented.

FIG. 2 is a block diagram of another example of an informationprocessing system within which one or more illustrative embodiments areimplemented.

FIG. 3 is a block diagram illustrating an example of a RAID 6 array forimplementation in the FIG. 1 system or the FIG. 2 system.

FIG. 4 is a table showing distribution of data blocks in the FIG. 3 RAID6 array.

FIG. 5 is a block diagram illustrating another example of a RAID 6 arrayfor implementation in the FIG. 1 system or the FIG. 2 system.

FIG. 6 is a table showing distribution of data blocks in the FIG. 5 RAID6 array.

FIG. 7 is a table showing an example of a stripe of a RAID 6 array withfree pages and taken pages with which one or more illustrativeembodiments can be implemented.

FIG. 8 is a block diagram of a content addressable storage systemcomprising a plurality of storage nodes configured to implementdistributed generation of data for prefilling of storage devices in anillustrative embodiment.

FIG. 9 is a flow diagram of a process for distributed generation of datafor prefilling of storage devices in an illustrative embodiment.

FIGS. 10A, 10B, 10C and 10D show examples of logical layer and physicallayer mapping tables in an illustrative embodiment.

FIGS. 11 and 12 show examples of processing platforms that may beutilized to implement at least a portion of an information processingsystem in illustrative embodiments.

DETAILED DESCRIPTION

Illustrative embodiments will be described herein with reference toexemplary information processing systems and associated computers,servers, storage devices and other processing devices. It is to beappreciated, however, that these and other embodiments are notrestricted to the particular illustrative system and deviceconfigurations shown. Accordingly, the term “information processingsystem” as used herein is intended to be broadly construed, so as toencompass, for example, processing systems comprising cloud computingand storage systems, as well as other types of processing systemscomprising various combinations of physical and virtual processingresources. An information processing system may therefore comprise, forexample, at least one data center or other cloud-based system thatincludes one or more clouds hosting multiple tenants that share cloudresources. Numerous different types of enterprise computing and storagesystems are also encompassed by the term “information processing system”as that term is broadly used herein.

FIG. 1 shows an information processing system 100 configured inaccordance with an illustrative embodiment. The information processingsystem 100 comprises a host device 102, which may comprise one of aplurality of host devices of a computer system. The host device 102communicates over a network 104 with first and second storage systems105-1 and 105-2, also denoted as Storage System 1 and Storage System 2,respectively. The storage systems 105-1 and 105-2 are collectivelyreferred to herein as storage systems 105. The host device 102 andstorage systems 105 may be part of an enterprise computing and storagesystem, a cloud-based system or another type of system.

The host device 102 and storage systems 105 illustratively compriserespective processing devices of one or more processing platforms. Forexample, the host device 102 and the storage systems 105 can eachcomprise one or more processing devices each having a processor and amemory, possibly implementing virtual machines and/or containers,although numerous other configurations are possible.

The host device 102 and the storage systems 105 can additionally oralternatively be part of cloud infrastructure such as an Amazon WebServices (AWS) system. Other examples of cloud-based systems that can beused to provide one or more of host device 102 and storage systems 105include Google Cloud Platform (GCP) and Microsoft Azure.

The host device 102 is configured to write data to and read data fromthe storage systems 105. The host device 102 and the storage systems 105may be implemented on a common processing platform, or on separateprocessing platforms. A wide variety of other types of host devices canbe used in other embodiments.

The host device 102 in some embodiments illustratively provides computeservices such as execution of one or more applications on behalf of eachof one or more users associated with the host device 102.

The term “user” herein is intended to be broadly construed so as toencompass numerous arrangements of human, hardware, software or firmwareentities, as well as combinations of such entities. Compute and/orstorage services may be provided for users under a platform-as-a-service(PaaS) model, an infrastructure-as-a-Service (IaaS) model and/or aFunction-as-a-Service (FaaS) model, although it is to be appreciatedthat numerous other cloud infrastructure arrangements could be used.Also, illustrative embodiments can be implemented outside of the cloudinfrastructure context, as in the case of a stand-alone computing andstorage system implemented within a given enterprise.

The network 104 is assumed to comprise a portion of a global computernetwork such as the Internet, although other types of networks can bepart of the network 104, including a wide area network (WAN), a localarea network (LAN), a satellite network, a telephone or cable network, acellular network, a wireless network such as a Win or WiMAX network, orvarious portions or combinations of these and other types of networks.The network 104 in some embodiments therefore comprises combinations ofmultiple different types of networks each comprising processing devicesconfigured to communicate using Internet Protocol (IP) or othercommunication protocols.

As a more particular example, some embodiments may utilize one or morehigh-speed local networks in which associated processing devicescommunicate with one another utilizing Peripheral Component Interconnectexpress (PCIe) cards of those devices, and networking protocols such asInfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternativenetworking arrangements are possible in a given embodiment, as will beappreciated by those skilled in the art.

The storage systems 105 are accessible to the host device over thenetwork 104. The storage system 105-1 comprises a plurality of storagedevices 106-1 and an associated storage controller 108-1. Similarly, thestorage system 105-2 comprises a plurality of storage devices 106-2 andan associated storage controller 108-2. The storage devices 106-1 and106-2 are collectively referred to herein as storage devices 106. Thestorage controllers 108-1 and 108-2 are collectively referred to asstorage controllers 108.

The storage devices 106 illustratively comprise solid state drives(SSDs). Such SSDs are implemented using non-volatile memory (NVM)devices such as flash memory. Other types of NVM devices that can beused to implement at least a portion of the storage devices 106 includenon-volatile random access memory (NVRAM), phase-change RAM (PC-RAM) andmagnetic RAM (MRAM). These and various combinations of multipledifferent types of NVM devices may also be used.

However, it is to be appreciated that other types of storage devices canbe used in other embodiments. For example, a given storage system as theterm is broadly used herein can include a combination of different typesof storage devices, as in the case of a multi-tier storage systemcomprising a flash-based fast tier and a disk-based capacity tier. Insuch an embodiment, each of the fast tier and the capacity tier of themulti-tier storage system comprises a plurality of storage devices withdifferent types of storage devices being used in different ones of thestorage tiers. For example, the fast tier may comprise flash driveswhile the capacity tier comprises hard disk drives (HDDs). Theparticular storage devices used in a given storage tier may be varied inother embodiments, and multiple distinct storage device types may beused within a single storage tier. The term “storage device” as usedherein is intended to be broadly construed, so as to encompass, forexample, flash drives or other types of SSDs, HDDs, hybrid drives orother types of storage devices.

In some embodiments, at least one of the storage systems 105illustratively comprises a scale-out all-flash content addressablestorage array such as an XtremIO™ storage array from Dell EMC ofHopkinton, Mass. Other types of storage arrays, including by way ofexample VNX® and Symmetrix VMAX® storage arrays also from Dell EMC, canbe used to implement one or both of storage systems 105 in otherembodiments.

The term “storage system” as used herein is therefore intended to bebroadly construed, and should not be viewed as being limited to contentaddressable storage systems or flash-based storage systems. A givenstorage system as the term is broadly used herein can comprise, forexample, network-attached storage (NAS), storage area networks (SANs),direct-attached storage (DAS) and distributed DAS, as well ascombinations of these and other storage types, includingsoftware-defined storage.

Other particular types of storage products that can be used inimplementing storage systems 105 in illustrative embodiments includeall-flash and hybrid flash storage arrays such as Unity™,software-defined storage products such as ScaleIO™ and ViPR®, cloudstorage products such as Elastic Cloud Storage (ECS), object-basedstorage products such as Atmos®, and scale-out NAS clusters comprisingIsilon® platform nodes and associated accelerators, all from Dell EMC.Combinations of multiple ones of these and other storage products canalso be used in implementing a given storage system in an illustrativeembodiment.

In the FIG. 1 embodiment, the storage devices 106 implement one or moreRAID arrays, denoted as RAID array 110-1 for storage devices 106-1 ofstorage system 105-1 and RAID array 110-2 for storage devices 106-2 ofstorage system 105-2. The RAID arrays 110-1 and 110-2 may collectivelyform a single larger RAID array, with the RAID arrays 110 representingdifferent portions of that single larger RAID array, or may instead beviewed as representing entirely distinct and separate RAID arrays. TheRAID arrays 110 are assumed to store data in stripes across a pluralityof SSDs provided by the storage devices 106. The RAID arrays 110implement examples of what is more generally referred to herein as datastriping across a plurality of storage devices in a storage system.

The host device 102 in the FIG. 1 embodiment includes a parity datacomputation module 112 which provides logic and functionality forcomputing parity data in a storage system that implements data stripingacross a plurality of storage devices (e.g., in RAID arrays 110 onstorage devices 106). Parity data computation will be described infurther detail below. The host device 102 should also be understood toinclude additional modules and other components typically found inconventional implementations of computers, servers or other hostdevices, although such additional modules and other components areomitted from the figure for clarity and simplicity of illustration.

The host device 102 and storage systems 105 in the FIG. 1 embodiment areassumed to be implemented using at least one processing platform eachcomprising one or more processing devices each having a processorcoupled to a memory. Such processing devices can illustratively includeparticular arrangements of compute, storage and network resources.

The host device 102 and the storage systems 105 may be implemented onrespective distinct processing platforms, although numerous otherarrangements are possible. For example, in some embodiments at leastportions of the host device 102 and one or both of the storage systems105 are implemented on the same processing platform. The storage systems105 can therefore be implemented at least in part within at least oneprocessing platform that implements at least a portion of the hostdevice 102.

The term “processing platform” as used herein is intended to be broadlyconstrued so as to encompass, by way of illustration and withoutlimitation, multiple sets of processing devices and associated storagesystems that are configured to communicate over one or more networks.For example, distributed implementations of the system 100 are possible,in which certain components of the system reside in one data center in afirst geographic location while other components of the system reside inone or more other data centers in one or more other geographic locationsthat are potentially remote from the first geographic location. Thus, itis possible in some implementations of the system 100 for the hostdevice 102 and storage systems 105 to reside in different data centers.Numerous other distributed implementations of one or both of the hostdevice 102 and the storage systems 105 are possible. Accordingly, thestorage systems 105 can also be implemented in a distributed manneracross multiple data centers.

Additional examples of processing platforms utilized to implement hostdevices and/or storage systems in illustrative embodiments will bedescribed in more detail below in conjunction with FIGS. 11 and 12.

It is to be appreciated that these and other features of illustrativeembodiments are presented by way of example only, and should not beconstrued as limiting in any way.

Accordingly, different numbers, types and arrangements of systemcomponents such as host device 102, network 104, storage systems 105,storage devices 106, storage controllers 108, and RAID arrays 110 can beused in other embodiments.

It should be understood that the particular sets of modules and othercomponents implemented in the system 100 as illustrated in FIG. 1 arepresented by way of example only. In other embodiments, only subsets ofthese components, or additional or alternative sets of components, maybe used, and such components may exhibit alternative functionality andconfigurations. Additional examples of systems implementingfunctionality for parity data computation in accordance with datastriping will be described below.

FIG. 2 shows an information processing system 200 configured inaccordance with another illustrative embodiment. The informationprocessing system 200 comprises a computer system 201 that includes hostdevices 202-1, 202-2, . . . 202-N collectively referred to as hostdevices 202. The host devices 202 communicate over a network 204 with astorage system 205. The computer system 201 is assumed to comprise anenterprise computer system, cloud-based computer system or otherarrangement of multiple compute nodes associated with respective users.The host devices 202 of the computer system 201 in some embodimentsillustratively provide compute services such as execution of one or moreapplications on behalf of each of one or more users associated withrespective ones of the host devices 202.

Similar to the storage systems 105 of system 100, the storage system 205comprises storage devices 206, storage controller 208 and RAID array210. However, in this embodiment, the functionality for parity datacomputation associated with data striping in RAID array 210 isimplemented in the storage system 205, rather than in one of the hostdevices 202. Accordingly, the storage controller 208 in this embodimentcomprises parity data computation module 212, which is configured tooperate in substantially the same manner as that described above forcorresponding module 112 of the host device 102 in the system 100.

In some embodiments, functionality for parity data computationassociated with data striping can be implemented partially in a hostdevice and partially in the storage system. For example, U.S. patentapplication Ser. No. 16/049,185, filed Jul. 30, 2018 and entitled“Efficient Computation of Parity Data in Storage System ImplementingData Striping,” which is incorporated by reference herein in itsentirety, discloses arrangements for utilizing processing capabilitieson the storage devices 106 themselves to perform a portion of a paritydata computation, results of which are then used for parity datacomputations on the host device (e.g., module 112) and/or the storagecontroller (e.g., module 212). Accordingly, illustrative embodiments arenot limited to arrangements in which all such functionality isimplemented in a host device or a storage system, and thereforeencompass various hybrid arrangements in which the functionality isdistributed over one or more host devices and one or more storagesystems, each comprising one or more processing devices.

Illustrative data striping operations with associated parity datacomputations in accordance with RAID based techniques will now bedescribed in further detail in the context of the information processingsystems 100 and 200. However, it is to be understood that data stripingoperations with associated parity data computations are more generallyapplicable to other types of information processing systems. At leastsome of the parity data computation steps are illustratively performedunder the control of the parity data computation module 112 in hostdevice 102 of system 100 or in module 212 in storage controller 208 ofsystem 200.

Data striping in some embodiments is implemented utilizing RAID, such asvia RAID arrays 110 on storage systems 105. In such embodiments, thenumber of data disks in the RAID storage system may comprise a primenumber k, and a column of the RAID storage system comprises k−1 blocks.The storage devices of the RAID storage system may be SSDs. The RAIDstorage system may implement RAID 6 with the number of data disks beingk and the number of parity disks being n, where n is greater than one(e.g., where n=2). In some embodiments, the stripe column size isselected as a multiple of a designated block size. The multiple may be aprime number minus 1. The prime number may be the same as or differentthan the prime numbers selected for different ones of the stripes.

In some cases, the prime number selected for a particular stripe may begreater than a number of the plurality of storage devices in the storagesystem that store data blocks for that stripe. To handle suchsituations, the parity blocks for the stripe may be computed by assumingor setting a set of virtual storage devices with pages storingdesignated predetermined values (e.g., zero pages). The particularnumber of virtual storage devices in the set may be equal to thedifference between the prime number selected for that stripe and thenumber of storage devices in the storage system which store data blocksfor that stripe.

The term RAID, as used herein, is an umbrella term for computer datastorage schemes that can divide and replicate data among multiplephysical disk drives. References to one or more “disks” in illustrativeembodiments disclosed herein are intended to be broadly construed, andare not limited to HDDs or other rotational media. For example, “disks”as that term is broadly used herein is intended to encompass other typesof storage drives such as SSDs. The terms “disks” and “drives” willtherefore be used interchangeably herein. The physical disks are said tobe in a RAID array, which is accessed by an operating system as onesingle disk. The different schemes or architectures are named by theword RAID followed by a number (e.g., RAID 0, RAID 1, etc.). Each schemeprovides a different balance between the goals of increasing datareliability and increasing input/output performance.

The RAID 6 scheme was developed to provide functionality for recoveringfrom multiple disk failures (e.g., similar to RAID 1.3) with highutilization rates (e.g., comparable to RAID 4 and 5) that avoids systembottlenecks. RAID 6 uses an N+2 parity scheme, which allows failure oftwo disks, where N is the number of disks in the array. RAID 6 definesblock-level striping with double distributed parity and provides faulttolerance of two drive failures, so that the array continues to operatewith up to two failed drives, irrespective of which two drives fail.

There are various implementations of RAID 6, which may use varyingcoding schemes. As the term is used herein, RAID 6 is defined as any N+2coding scheme which tolerates double disk failure, while user data iskept in the clear. This additional requirement assures that user readsare not affected by the RAID scheme under normal system operation.Examples of RAID 6 schemes include those that utilize the Reed Solomonerror correction code and those that utilize parity bits, such as thosewherein N data disks are supported by two redundancy disks each holdinga different parity bit. It should be noted that if all parity bits areon the same two disks, then the performance may be subject tobottlenecks. This can be solved by use of distributed parity stripesover N+2 disks similar to that specified in RAID 5. Examples of codingschemes based on parity calculations of rows and diagonals in a matrixof blocks include Even/Odd and Row Diagonal Parity (RDP). Both of theseschemes utilize a first parity disk P that holds the parities of rows ofblocks as well as a second parity disk Q that contains blocks that holdthe parity of diagonals of data blocks. In both schemes, it isadvantageous to work with a block size that is smaller than the nativepage size. For example, the native page size may be 8 KB, while theblock size is smaller but evenly divisible into 8 KB, e.g., 0.5 KB, 1KB, 2 KB, 4 KB. In an example where the native page size is 8 KB and theblock size is 2 KB, each stripe thus may contain four rows, and thus thefour blocks present on each disk form a single native page. However, astripe can also be defined by multiple rows of blocks distributed acrossthe storage devices of the RAID array. It is assumed that pages are readand written using a single disk operation.

FIG. 3 shows a RAID array 300, which in this example includes five datadisks denoted D0 through D4. A storage controller such as storagecontrollers 108 or storage controller 208) is configured for writinginitial data into the array 300, and for updating existing data in thearray 300. The storage controller further provides functionality forrecovering data after single or double disk failure.

Each of the disks in the array 300 stores a column of data blocks. Thesame data block in successive disks forms a row, which is to say therows cross the disks. The data storage blocks are stored alongsideparity data blocks in parity disks denoted P and Q, and the numbers ofdata blocks in the different columns or disks may be different. Rowparity blocks are placed in a row parity column in disk P, and thediagonal parity data is placed in diagonal parity blocks in disk Q. Notethat parity data stored in parity disks P and Q is computed inaccordance with parity data computation module 112 (FIG. 1 system) orparity data computation module 212 (FIG. 2 system).

In the case of five data columns and four data rows, the number ofdiagonals is one greater than the number of rows. Thus, the diagonalparity column in disk Q includes one more block than the other columnsfor disks D0 through D4 and the row parity disk P. This is illustratedin FIG. 3 as Q is “taller” than D0 through D4 and P.

The number of data columns is a prime number, and the number of rows isone less than that prime number (e.g., in the FIG. 3 example the primenumber is 5 corresponding to the five data disks D0 through D4). Itshould be noted that, in practice, the various columns are distributedover the available physical disks to avoid system bottlenecks.

FIG. 4 shows a table 400 illustrating one example distribution of datablocks in the RAID array 300. In this case, there are k data disks,where k=5 is a prime number and there are five data columnscorresponding to disks D0 through D4. There are four rows (e.g., k−1).The P column includes the same four rows as the data columns D0 throughD4, but the Q column has an extra row.

In one example, each stripe is considered to contain k (where k must beprime) data columns D0 through D4, and two parity columns P and Q. Thestripe is composed of a quasi-matrix of blocks, which contains k−1 rows.Column P contains k−1 blocks, each providing the parity of the k datadisk blocks in its row. The k by k−1 matrix made up of the blocks in thedata columns includes k diagonals each of size k−1. Column Q, incontrast with the rest of the columns, contains k blocks and not k−1.Each of the k blocks in disk Q holds the parity of one of the kdiagonals. It should be noted that the ordering of blocks within eachcolumn may be arbitrary. Furthermore, the extra block in column Q may beplaced in a data column which does not contain a data block in thediagonal of which this block is the parity. Also, some of the rows maybe blank.

It should be appreciated that there are various other ways to distributedata blocks in an array such as RAID array 300. For example, in soniccases it may be desired to provide more than one row parity column,which results in higher capacity overhead but which allows for a fasterrebuild after a single disk failure.

Additional details regarding the above-described techniques of FIGS. 3and 4 for storing data in RAID arrays are disclosed in U.S. Pat. No.9,552,258, entitled “Method and System for Storing Data in RAID MemoryDevices,” which is incorporated by reference herein.

FIG. 5 shows a RAID array 500 which, similar to RAID array 300 in FIG.3, includes five data disks denoted D0 through D4. Similarly, a storagecontroller (e.g., such as storage controllers 108 or storage controller208) is configured for writing initial data into the array 500, and forupdating existing data in the array 500. The storage controller furtherprovides functionality for recovering data after single or double diskfailure.

Similar to array 300, each of the disks in the array 500 stores a columnof data blocks. The same data block in successive disks forms a row.Further, the data storage blocks are stored alongside parity data blocksin parity disks denoted P and Q, where row parity blocks are placed in arow parity column in disk P, and the diagonal parity data is placed inparity blocks in disk Q. Note again that parity data stored in paritydisks P and Q is computed in accordance with parity data computationmodule 112 (FIG. 1 system) or parity data computation module 212 (FIG. 2system).

Recall that in array 300, the diagonal parity column in disk Q includesone more block than the other columns for disks D0 through D4 and therow parity disk P (i.e., in the case of five data columns and four datarows, the number of diagonals is one greater than the number of rows).However, in array 500, Q has the same number of blocks as D0 through D4and P and therefore is the same size as the other disks, as illustratedin FIG. 5. This is because array 500 utilizes “column parity data” thatis computed and combined with diagonal parity data to eliminate theextra block in disk Q. Computation of column parity data will be furtherexplained below.

FIG. 6 shows a table 600 illustrating one example distribution of datablocks in the RAID array 500. The implementation of FIGS. 5 and 6 isreferred to as an updated RAID 6 implementation. In this case, as withtable 400 for array 300, there are k data disks, where k=5 is a primenumber and there are five data columns corresponding to disks D0 throughD4. There are four rows (e.g., k−1). The P column includes the same fourrows as the data columns D0 through D4, but in this embodiment, unliketable 400, the Q column has the same number of rows as D0 through D4 andP.

In table 600, row parity data and diagonal parity data are computed asdescribed above with respect to table 400. However, parity computationin the embodiment of FIGS. 5 and 6 avoids the extra block in disk Q byadding per column parity to the diagonals. This approach introduces anadditional cost of the need to calculate the column parity. Columnparity provides an updated RAID 6 scheme as follows:

Let S be a stripe and mark S_(i,j):=the block in row i column j.

For every disk j column of the stripe, let d_(j):=⊕_(i=1)^(p−1)S_(i,j)∀i∈{1 . . . p−1}.

Define p_(i):=parity of row i and q_(i):=parity of diagonal i. Bydiagonal i, we refer to the diagonal that is not intersecting withcolumn i (as explained above, non-existent columns are just consideredas predefined zeros).

Let q_(p) be the extra Q block.

Define q _(k):=q_(k)⊕d_(k) where the symbol ⊕ refers to an XORoperation. The XOR operation is a Boolean operation used at the binarylevel to create RAID parity. This operation is the exclusive disjunctionoperation also known as exclusive OR (XOR). In the XOR operation (alsoreferred to as XOR'ing, performing XOR, XOR computation, etc.), binarydata is passed through logic that embodies the XOR operation and resultsin a binary result, which is used for redundancy and error correction asdescribed herein. In such case, the result of the XOR operation isreferred to as parity data.

Thus, q_(k) is referred to in table 600 as “diagonal parity” and d_(k)is referred to as “column parity.” As such, q_(k)⊕d_(k) is referred toin table 600 as “diagonal parity and column parity.” Further, disk Q isdenoted as Q in table 600.

Thus, given P and Q, the updated RAID 6 implementation described inFIGS. 5 and 6 enables recovery from a double failure. In variousembodiments, if a data disk and a Q parity disk fails, recovery in anupdated RAID 6 implementation continues as normal.

In certain embodiments, if data disk i≠p and disk P fail, d₁ is knownfor all i≠j, and d_(i) can be recovered from q _(i) since the diagonal iis not intersecting column i thus q_(i) is known. In some embodiments,XOR'ing out the d_(j) from Q bring us to the known recovery formula. Incertain embodiments, if i=p then Q is known since all d_(j) are known,and each block may be recovered from the diagonal.

In some embodiments, if two data disks fail (disk i and disk j) whereneither failed disk is the parity disk P. the updated RAID 6implementation enables recovery of the blocks using two steps. In one ormore embodiments, a first step includes partially recovering each block.Ŝ _(k,i) =Ŝ _(k,i)⊕{either d _(i) or d _(j)}Ŝ _(k,j) =Ŝ _(k,j)⊕{either d _(i) or d _(j)}

In one or more embodiments, a second step includes XOR'ing out d_(i) andd_(j) to get the data block S_(k,i) and S_(k,j). Since p−1 is even, rand p−1−r are even/odd together, if r is even then an XOR on all blocksof the column i eliminates d_(j) and d_(i) and thus we will get d_(i)and in the same way we can get d_(j), and thus recover the data. If r isodd, then we get ⊕_(i=1) ^(r)S_(k,j)⊕_(i=r+1)^(p−1−r)S_(k,j)⊕d_(j)⊕d_(i)=d_(i) thus we get d_(i) and d_(j) and we maycontinue recovery. In some embodiments, the case of i=p is just aspecial case with r=0.

In one or more embodiments, an updated RAID 6 implementation such asthat depicted in FIGS. 5 and 6 provides many advantages. For example, aparity disk failure causes, at most, reading 2*P+1 blocks fordouble-degraded read. Still further, upon write of a single block, atmost three parities are updated (row parity, diagonal parity, and thediagonal XOR'd with the column parity).

Note again that, in some embodiments, parity data stored in parity disksP and Q, including column parity data and the combined diagonal parityand column parity data, is computed in accordance with parity datacomputation module 112 (FIG. 1 system) or parity data computation module212 (FIG. 2 system).

Additional details regarding the above-described techniques of FIGS. 5and 6 for storing data in RAID arrays are disclosed in U.S. Pat. No.9,891,994, entitled “Updated RAID 6 Implementation,” which isincorporated by reference herein.

FIG. 7 shows a table 700 illustrating another example data stripingdistribution with column parity data combined with diagonal parity dataas explained above in the context of FIGS. 5 and 6. In table 700, astripe S is depicted with prime equal to 41 and 34 data disks SSD 1through SSD 34. Note that it is assumed that the disks are SSDs.Further, SSD 35 is the row parity disk (disk P above) and SSD 36 is thecombined diagonal party and column parity disk (disk Q above). Note thateach block in the data disks is labeled either T for taken space (notavailable since the space contains data) or F for free space (availablespace since the space does not contain data).

One approach for calculating the column parity d_(j) (column paritydata) is: reading the column from the drive and performing the XORoperations with the diagonal parity data, as explained above, in thehost device (e.g., module 112 in FIG. 1) or the storage controller(e.g., module 212 in FIG. 2); adding new pages instead of the old pages;and keeping all the rest of the data pages as they are. This introducessignificant read amplification in case the stripe is not empty, as wellas CPU consumption for the parity calculations (XOR operations).

For example, assume a page size is 8 KB, and a stripe has 40 rows and 34data columns as in table 700, and assume the stripe is fifty percentfull (50% of the blocks are non-free or taken (T) and 50% are free (F)).Then, there are about 680 pages free (pages that can be overwritten). Ifthey are well distributed across the SSDs, this means there are 20 freepages in each column. For each column, the entire 20 (i.e., 40 minus 20)pages have to be read to recalculate the column (for the stripe, 680pages in total need to be read), resulting in an additional readoperation for every page to update, as well as additional XOR operationsof every column. It is also realized that the read amplification getsworse as the stripe is fuller.

An alternative to reading the entire column from the drive is to savethe column data which is optimal in terms of bandwidth and CPUconsumption but is very wasteful in terms of SSD capacity.

Illustrative embodiments described in the above-cited U.S. patentapplication Ser. No. 16/049,185 overcome the above and other drawbacksassociated with column parity data computations by utilizing processingcapabilities of the storage devices (e.g., SSDs) themselves to performat least a portion of the column parity data computations. For example,SSDs illustratively comprise flash drives that are equipped withprocessing capabilities such as, for example, an internal centralprocessing unit (CPU) and/or a hardware accelerator unit, One or moresuch processing capabilities are referred to herein as “a processingdevice” of the SSD. As described in the above-cited U.S. patentapplication Ser. No. 16/049,185, such processing capabilities of theSSDs are used to perform operations over data in the drives withoutinvolving the host device (e.g., 102 in FIG. 1) or storage controllers(e.g., 108 in FIG. 1 or 208 in FIG. 2). The results of the operationsperformed by the internal processing capabilities of the SSDs are madeavailable to the host device or storage controllers to perform furtheroperations.

More particularly, optimization of the column calculations leverages SSDprocessing capabilities which, in turn, optimizes both the bandwidth tothe drives, as well as saving the CPU of the host device or storagecontroller needed for calculating the column parity.

For each column (i.e., a column of blocks resides on a specific drive),a command (instruction) is sent from a parity data computation module(112 in host device 102 or module 212 in storage controller 208) to thegiven drive to calculate the XOR. result of all non-free pages in thecolumn using internal CPU and/or hardware acceleration engines of thegiven drive. The result of the XOR operation is read from the givendrive by the parity data computation module (112 or 212). Then, bycombining the results read from the given drive with the new data to bewritten to the free space, the column XOR operation is completed.Additional details can be found in the above-cited U.S. patentapplication Ser. No. 16/049,185.

Additional illustrative embodiments will now be described. Theseembodiments may but need not utilize the above-described paritycomputation techniques, and can be implemented in a wide variety ofother types of storage systems using different RAID and paritycomputation arrangements.

A first group of additional illustrative embodiments provides techniquesfor distributed generation of random data in a storage array or othertype of storage system. In other embodiments of this type, the generateddata need not be random data, but instead may be another type of datasuitable for use in prefilling operations or other types of operationsimplemented within a storage system or associated host device.

In some embodiments, a distributed storage system comprises a cluster ofstorage nodes implementing a RAID array. The RAID array comprises one ormore storage devices on each of the storage nodes, and each storage nodeis illustratively running software responsible for a portion of thecompute power and drives of the RAID array. As indicated previously,conventional techniques for prefilling a RAID array with suitableamounts and types of data are inefficient and unduly time consuming.

This inefficiency is attributable to a number of different factors. Forexample, due to the additional parity calculation overhead, it takeslonger to write a block in a RAID array that has previously been writtento rather than a fresh, never written to block. In order for performancetesting to reflect a system that has been in use for some time, all ofthe “free” blocks must have been written to in the past.

Also, when prefilling the physical stripes of the RAID array, it isdesirable to generate random data blocks that are unique anduncompressible. However, this is an expensive operation, especiallysince the random data generated may be appropriate for a differentstorage node in the RAID array than the one that generates the randomdata.

In some embodiments, a content addressable storage system uses a hash onthe data to determine the location of the data. In this case, data witha non-matching hash would need to be discarded and regenerated and thehash recalculated, which are costly operations. In a system with fourhash domains (e.g., nodes or processes) the probability of generatingthe right data is 25%, and with a larger system this probability dropsfurther.

The embodiments to be described below in conjunction with FIGS. 8, 9 and10A-10D overcome the above-described problems of conventional approachesthrough distributed generation of data.

In some of these embodiments, a storage system comprises a plurality ofstorage nodes each comprising one or more storage devices, with each ofthe storage nodes further comprising a processor coupled to a memory.One or more of the storage nodes are each configured to generate datablocks for prefilling of at least a subset of the storage devices, tomaintain a first portion of the generated data blocks in a pool of datablocks available in the given storage node for prefilling one or more ofthe storage devices of the given storage node, and to send a secondportion of the generated data blocks from the given storage node to oneor more other ones of the storage nodes for prefilling one or more otherones of the storage devices of the one or more other storage nodes. Thefirst and second portions of the generated data blocks eachillustratively comprise at least one generated data blocks.

The data blocks in some embodiments are randomly generated, althoughother arrangements are possible in other embodiments.

A given one of the storage nodes may be configured to monitor the poolof data blocks available in the given storage node for prefilling one ormore of the storage devices of the given storage node, and responsive tothe number of data blocks in the pool being at or below a designatedthreshold, to send a request to one or more of the other storage nodesfor additional data blocks generated by the one or more other storagenodes. Each of the other storage nodes illustratively generates one ormore of the additional data blocks and sends those one or moreadditional data blocks to the given storage node.

The given storage node in some embodiments computes a content-basedsignature for a particular one of the generated data blocks, andresponsive to the computed content-based signature for the particularone of the generated data blocks being within a portion of acontent-based signature space assigned to the given storage node, placesthe particular generated data block in the pool of data blocks availablein the given storage node for prefilling one or more of the storagedevices of the given storage node. Responsive to the content-basedsignature for the particular one of the generated data blocks not beingwithin a portion of a content-based signature space assigned to thegiven storage node, the given storage node identifies another one of thestorage nodes for which the content-based signature for the particularone of the generated data blocks is within a portion of thecontent-based signature space assigned to that other storage node, andsends the particular generated data block to that other storage node.

Referring now to FIG. 8, an example storage system of the type describedabove is shown in more detail. This figure shows a content addressablestorage system 805 comprising a plurality of storage devices 806 and anassociated storage controller 808. The content addressable storagesystem 805 may be viewed as a particular implementation of a given oneof the storage systems 105 or 205, and accordingly is assumed to becoupled to one or more host devices such as host device 102 or hostdevices 202. The content addressable storage system 805 is configured toimplement functionality for distributed generation of data forprefilling of the storage devices 806 in an illustrative embodiment.

The storage controller 808 includes distributed modules 812, 814 and816, illustratively distributed across a plurality of storage nodes 815of the content addressable storage system 805. The content addressablestorage system 805 in the FIG. 8 embodiment is therefore implemented asa clustered storage system comprising storage nodes 815 each of whichcomprises a corresponding subset of the storage devices 806. Otherclustered storage system arrangements comprising multiple storage nodesand possibly additional or alternative nodes can be used in otherembodiments. A given clustered storage system may therefore include notonly storage nodes 815 but also additional storage nodes, compute nodesor other types of nodes coupled to a network such as network 104 ornetwork 204. Alternatively, such additional storage nodes may be part ofanother clustered storage system of the system 100 or 200. Each of thestorage nodes 815 of the storage system 805 is assumed to be implementedusing at least one processing device comprising a processor coupled to amemory.

The distributed modules 812, 814 and 816 more particularly comprisedistributed prefilling control logic 812, distributed data blockgenerators 814, and distributed data block pools 816. It is assumed thateach of the distributed modules 812, 814 and 816 comprises multiplemodule instances implemented on respective ones of the storage nodes815. These module instances are further assumed to be part of thestorage controller 808. The distributed modules 812, 814 and 816collectively implement the distributed data generation and associatedRAID array prefilling algorithm shown in the flow diagram of FIG. 9.

As indicated above, the storage controller 808 of the contentaddressable storage system 805 is implemented in a distributed manner soas to comprise a plurality of distributed storage controller componentsimplemented on respective ones of the storage nodes 815. The storagecontroller 808 is therefore an example of what is more generallyreferred to herein as a “distributed storage controller.” In subsequentdescription herein, the storage controller 808 is referred to asdistributed storage controller 808.

Each of the storage nodes 815 in this embodiment further comprises a setof processing modules configured to communicate over one or morenetworks with corresponding sets of processing modules on other ones ofthe storage nodes 815. The sets of processing modules of the storagenodes 815 collectively comprise at least a portion of the distributedstorage controller 808 of the content addressable storage system 805.

The modules of the distributed storage controller 808 in the presentembodiment more particularly comprise different sets of processingmodules implemented on each of the storage nodes 815. The set ofprocessing modules of each of the storage nodes 815 comprises at least acontrol module 808C, a data module 808D and a routing module 808R. Thedistributed storage controller 808 further comprises one or moremanagement (“MGMT”) modules 808M. For example, only a single one of thestorage nodes 815 may include a management module 808M. It is alsopossible that management modules 808M may be implemented on each of atleast a subset of the storage nodes 815. A given set of processingmodules implemented on a particular one of the storage nodes 815therefore illustratively includes at least one control module 808C, atleast one data module 808D and at least one routing module 808R, andpossibly a management module 808M.

Terms such as “control module,” “data module,” “routing module” and“management module” as used herein are intended to be broadly construed,so as to encompass, for example, different processing modules of aserver implemented by a storage node running on a processing device of aclustered storage system. It is also possible that such modules maycomprise respective separate nodes or other separate processing devicesof a clustered storage system.

Communication links may be established between the various processingmodules of the distributed storage controller 808 using well-knowncommunication protocols such as IP, Transmission Control Protocol (TCP),and remote direct memory access (RDMA). For example, respective sets ofIP links used in data transfer and corresponding messaging could beassociated with respective different ones of the routing modules 808R.

Although shown as separate modules of the distributed storage controller808, the modules 812, 814 and 816 in some embodiments may be distributedat least in part over at least a subset of the other modules 808C, 808D,808R and 808M of the storage controller 808. Accordingly, at leastportions of the distributed data generation and associated RAID arrayprefilling functionality of the modules 812, 814 and 816 may beimplemented in one or more of the other modules of the storagecontroller 808. In other embodiments, the modules 812, 814 and 816 maybe implemented as stand-alone modules of the storage controller 808.

The storage devices 806 are configured to store metadata pages 820 anduser data pages 822, and may also store additional information notexplicitly shown such as checkpoints and write journals. The metadatapages 820 and the user data pages 822 are illustratively stored inrespective designated metadata and user data areas of the storagedevices 806. Accordingly, metadata pages 820 and user data pages 822 maybe viewed as corresponding to respective designated metadata and userdata areas of the storage devices 806.

A given “page” as the term is broadly used herein should not be viewedas being limited to any particular range of fixed sizes. In someembodiments, a page size of 8 kilobytes (KB) is used, but this is by wayof example only and can be varied in other embodiments. For example,page sizes of 4 KB, 16 KB or other values can be used. Accordingly,illustrative embodiments can utilize any of a wide variety ofalternative paging arrangements for organizing the metadata pages 820and the user data pages 822.

The user data pages 822 are part of a plurality of LUNs configured tostore files, blocks, objects or other arrangements of data, each alsogenerally referred to herein as a “data item,” on behalf of users of thecontent addressable storage system 805. Each such LUN may compriseparticular ones of the above-noted pages of the user data area. The userdata stored in the user data pages 822 can include any type of user datathat may be utilized in the system 100. The term “user data” herein istherefore also intended to be broadly construed.

A given storage volume for which content-based signatures are generatedby storage controller 808 illustratively comprises a set of one or moreLUNs, each including multiple ones of the user data pages 822 stored instorage devices 806.

The content addressable storage system 805 in the embodiment of FIG. 8is configured to generate hash metadata providing a mapping betweencontent-based digests of respective ones of the user data pages 822 andcorresponding physical locations of those pages in the user data area.Content-based digests generated using hash functions are also referredto herein as “hash digests.” Such hash digests or other types ofcontent-based digests are examples of what are more generally referredto herein as “content-based signatures” of the respective user datapages 822. The hash metadata generated by the content addressablestorage system 805 is illustratively stored as metadata pages 820 in themetadata area. The generation and storage of the hash metadata isassumed to be performed under the control of the storage controller 808.

Each of the metadata pages 820 characterizes a plurality of the userdata pages 822. For example, in a given set of n user data pagesrepresenting a portion of the user data pages 822, each of the user datapages is characterized by a LUN identifier, an offset and acontent-based signature. The content-based signature is generated as ahash function of content of the corresponding user data page.Illustrative hash functions that may be used to generate thecontent-based signature include the SHA1 secure hashing algorithm, orother secure hashing algorithms known to those skilled in the art,including SHA2, SHA256 and many others. The content-based signature isutilized to determine the location of the corresponding user data pagewithin the user data area of the storage devices 806.

Each of the metadata pages 820 in the present embodiment is assumed tohave a signature that is not content-based. For example, the metadatapage signatures may be generated using hash functions or other signaturegeneration algorithms that do not utilize content of the metadata pagesas input to the signature generation algorithm. Also, each of themetadata pages is assumed to characterize a different set of the userdata pages.

A given set of metadata pages representing a portion of the metadatapages 820 in an illustrative embodiment comprises metadata pages havingrespective signatures, Each such metadata page characterizes a differentset of n user data pages. For example, the characterizing information ineach metadata page can include the LUN identifiers, offsets andcontent-based signatures for each of the n user data pages that arecharacterized by that metadata page. It is to be appreciated, however,that the user data and metadata page configurations described above areexamples only, and numerous alternative user data and metadata pageconfigurations can be used in other embodiments.

Ownership of a user data logical address space within the contentaddressable storage system 805 is illustratively distributed among thecontrol modules 808C.

The functionality for distributed data generation and associated RAIDarray prefilling provided by modules 812, 814 and 816 in someembodiments can be distributed across at least a subset of theprocessing modules 808C, 808D, 808R and 808M of the distributed storagecontroller 808.

In some embodiments, the content addressable storage system 805comprises an XtremIO™ storage array suitably modified to incorporatefunctionality for distributed generation of data as disclosed herein.

In arrangements of this type, the control modules 808C, data modules808D and routing modules 808R of the distributed storage controller 808illustratively comprise respective C-modules, D-modules and R-modules ofthe XtremIO™ storage array. The one or more management modules 808M ofthe distributed storage controller 808 in such arrangementsillustratively comprise a system-wide management module (“SYM module”)of the XtremIO™ storage array, although other types and arrangements ofsystem-wide management modules can be used in other embodiments.Accordingly, functionality for distributed data generation andassociated RAID array prefilling in some embodiments is implementedunder the control of at least one system-wide management module of thedistributed storage controller 808, utilizing the C-modules, D-modulesand R-modules of the XtremIO™ storage array.

In the above-described XtremIO™ storage array example, each user datapage has a fixed size such as 8 KB and its content-based signature is a20-byte signature generated using the SHA1 secure hashing algorithm.Also, each page has a LUN identifier and an offset, and so ischaracterized by <lun_id, offset, signature>.

The content-based signature in the present example comprises acontent-based digest of the corresponding data page. Such acontent-based digest is more particularly referred to as a “hash digest”of the corresponding data page, as the content-based signature isillustratively generated by applying a hash function such as the SHA1secure hashing algorithm to the content of that data page. The full hashdigest of a given data page is given by the above-noted 20-bytesignature. The hash digest may be represented by a corresponding “hashhandle,” which in some cases may comprise a particular portion of thehash digest. The hash handle illustratively maps on a one-to-one basisto the corresponding full hash digest within a designated clusterboundary or other specified storage resource boundary of a given storagesystem. In arrangements of this type, the hash handle provides alightweight mechanism for uniquely identifying the corresponding fullhash digest and its associated data page within the specified storageresource boundary. The hash digest and hash handle are both consideredexamples of “content-based signatures” as that term is broadly usedherein.

Examples of techniques for generating and processing hash handles forrespective hash digests of respective data pages are disclosed in U.S.Pat. No. 9,208,162, entitled “Generating a Short Hash Handle,” and U.S.Pat. No. 9,286,003, entitled “Method and Apparatus for Creating a ShortHash Handle Highly Correlated with a Globally-Unique Hash Signature,”both of which are incorporated by reference herein.

As mentioned previously, storage controller components in an XtremIO™storage array illustratively include C-module, D-module and R-modulecomponents. For example, separate instances of such components can beassociated with each of a plurality of storage nodes in a clusteredstorage system implementation.

The distributed storage controller in this example is configured togroup consecutive pages into page groups, to arrange the page groupsinto slices, and to assign the slices to different ones of theC-modules. For example, if there are 1024 slices distributed evenlyacross the C-modules, and there are a total of 16 C-modules in a givenimplementation, each of the C-modules “owns” 1024/16=64 slices. In sucharrangements, different ones of the slices are assigned to differentones of the control modules 808C such that control of the slices withinthe storage controller 808 of the storage system 805 is substantiallyevenly distributed over the control modules 808C of the storagecontroller 808.

The D-module allows a user to locate a given user data page based on itssignature. Each metadata page also has a size of 8 KB and includesmultiple instances of the <lun_id, offset, signature> for respectiveones of a plurality of the user data pages. Such metadata pages areillustratively generated by the C-module but are accessed using theD-module based on a metadata page signature.

The metadata page signature in this embodiment is a 20-byte signaturebut is not based on the content of the metadata page. Instead, themetadata page signature is generated based on an 8-byte metadata pageidentifier that is a function of the LUN identifier and offsetinformation of that metadata page.

If a user wants to read a user data page having a particular LUNidentifier and offset, the corresponding metadata page identifier isfirst determined, then the metadata page signature is computed for theidentified metadata page, and then the metadata page is read using thecomputed signature. In this embodiment, the metadata page signature ismore particularly computed using a signature generation algorithm thatgenerates the signature to include a hash of the 8-byte metadata pageidentifier, one or more ASCII codes for particular predeterminedcharacters, as well as possible additional fields. The last bit of themetadata page signature may always be set to a particular logic value soas to distinguish it from the user data page signature in which the lastbit may always be set to the opposite logic value.

The metadata page signature is used to retrieve the metadata page viathe D-module. This metadata page will include the <lun_id, offset,signature> for the user data page if the user page exists. The signatureof the user data page is then used to retrieve that user data page, alsovia the D-module.

Write requests processed in the content addressable storage system 805each illustratively comprise one or more IO operations directing that atleast one data item of the storage system 805 be written to in aparticular manner. A given write request is illustratively received inthe storage system 805 from a host device over a network. In someembodiments, a write request is received in the distributed storagecontroller 808 of the storage system 805, and directed from oneprocessing module to another processing module of the distributedstorage controller 808. For example, a received write request may bedirected from a routing module 808R of the distributed storagecontroller 808 to a particular control module 808C of the distributedstorage controller 808. Other arrangements for receiving and processingwrite requests from one or more host devices can be used.

The term “write request” as used herein is intended to he broadlyconstrued, so as to encompass one or more IO operations directing thatat least one data item of a storage system be written to in a particularmanner. A given write request is illustratively received in a storagesystem from a host device.

In the XtremIO™ context, the C-modules, D-modules and R-modules of thestorage nodes 815 communicate with one another over a high-speedinternal network such as an InfiniBand network. The C-modules, D-modulesand R-modules coordinate with one another to accomplish various IOprocessing tasks.

The write requests from the host devices identify particular data pagesto be written in the storage system 805 by their corresponding logicaladdresses each comprising a LUN ID and an offset.

As noted above, a given one of the content-based signaturesillustratively comprises a hash digest of the corresponding data page,with the hash digest being generated by applying a hash function to thecontent of that data page. The hash digest may be uniquely representedwithin a given storage resource boundary by a corresponding hash handle.The hash digest and the hash handle are each considered examples of whatare more generally referred to herein as content-based signatures of agiven data page. The hash digest and hash handle of a given data pageare also referred to as simply a “hash” of that data page. The datapages are examples of what are more generally referred to herein as“data blocks.”

Referring now to FIG. 9, an example process for distributed generationof data for prefilling of a RAID array comprising at least a portion ofthe storage devices 806 of the content addressable storage system 805 isshown. It should be noted that the term “RAID array” as used herein isintended to be broadly construed, so as to encompass any storage systemthat utilizes a RAID arrangement to distribute data across multiplestorage devices. The process in this embodiment comprises an examplealgorithm collectively implemented by the distributed modules 812, 814and 816. The process includes steps 900 through 918, which arecollectively performed by different instances of the modules 812, 814and 816 deployed on respective ones of the storage nodes 815.

The steps are more particularly performed by a particular one of thestorage nodes, denoted as a “first storage node,” through interactionwith other ones of the storage nodes. It is assumed that each of theother storage nodes also performs operations similar to those performedby the first storage node.

Moreover, although steps 900-918 are shown in the figure as beingperformed in a particular sequence, this is by way of example only, forclarity and simplicity of illustration, and different ones of the stepsor sets of the steps can instead be performed concurrently, or otherwiseperformed in an asynchronous manner relative to one another. Theparticular configuration of the flow diagram should therefore not beviewed as indicating that synchronous performance of steps 900-918 isrequired.

References to storage nodes in the following description of the FIG. 9process illustratively refer to particular ones of the storage nodes815, and references to storage devices illustratively refer to subsetsof storage devices 806 associated with different ones of the storagenodes 815, although numerous other arrangements of storage nodes andassociated storage devices are possible in other embodiments.

In step 900, the first storage node generates a data block forprefilling of the subset of storage devices associated with that storagenode. It is assumed that the storage devices do not contain any userdata pages prior to the prefilling of those storage devices, althoughother arrangements are possible. The term “prefilling” as used herein istherefore intended to be broadly construed, so as to encompass, forexample, arrangements in which storage devices that may contain someamount of data are filled with additional data. Also, the prefillingneed not be to a full capacity of the storage devices, but can insteadbe, for example, to some designated percentage of the full capacity.

The data blocks referred to in this embodiment are assumed to compriserespective random data blocks. The term “random” as used herein isintended to be broadly construed, so as to encompass pseudorandomarrangements, in which data blocks are generated using a pseudorandomnumber generator (PRNG) or other similar type of data generationcircuitry well known to those skilled in the art. Other embodimentsgenerate data blocks that are not random data blocks. For example, someembodiments disclosed herein perform prefilling of a RAID array usingdata blocks that are read from storage devices of the storage nodes,rather than randomly generated by the storage nodes.

In step 902, the first storage node generates a hash of the data block.In some embodiments, the data block comprises a data page of the typedescribed elsewhere herein. As indicated previously, the hash can referto a hash digest or a hash handle, or other type of content-basedsignature of the data block. Other types of data blocks can be used inother embodiments, and the term “data block” as used herein shouldtherefore not be viewed as being limited to a data page or otherparticular size or type of block.

In step 904, a determination is made as to whether or not the hash ofthe data block falls within a particular portion of a hash space of thestorage system, where the particular portion is the portion of the hashspace assigned to the first storage node. The hash space is an exampleof what is more generally referred to herein as a “content-basedsignature space” of the storage system. In some embodiments, differentportions of a logical address space of the storage system are assignedto respective different ones of the control modules of the storagenodes, and different portions of a content-based signature space of thestorage system are assigned to respective different ones of the datamodules of the storage nodes. Numerous other arrangements involvingassignments of different portions of logical address spaces andcontent-based signature spaces of a storage system to different storagenodes of the storage system can be used in other embodiments. The “hashspace” can comprise, for example, a hash digest space or a hash handlespace, or other type of designated hash space. If the hash of the datablock falls within the particular portion of the hash space assigned tothe first storage node, the process moves to step 906, and otherwisemoves to step 908 as indicated.

In step 906, the first storage node adds the data block to a pool ofavailable data blocks maintained by the first storage node. Such datablocks are “appropriate” for the first storage node because they haverespective hashes that fall within the assigned portion of the hashspace. The data blocks in the pool maintained by the first storage nodeare therefore available for use by the first storage node in prefillingstorage devices of that storage node. The process then moves to step 910as indicated.

In step 908, which is reached responsive to a mismatch between the hashof the data block and the portion of the hash space assigned to thefirst storage node, the first storage node sends the data block toanother storage node for which the hash of the data block falls withinthe portion of the hash space assigned to that storage node. In otherwords, if the first storage node determines in step 904 that the datablock generated in step 900 is appropriate not for itself, but isinstead appropriate for another one of the storage nodes, the firststorage node sends the data block to that other storage node. It istherefore assumed in this embodiment that each of the storage nodes ofthe storage system maintains a separate pool of generated data blocksfor prefilling its corresponding storage devices. Each of these separatepools stores data blocks that are appropriate for its correspondingstorage node, namely, data blocks having respective hashes that fallwithin its corresponding assigned portion of the hash space of thestorage system. A given such data block may be generated by thecorresponding storage node, or received from another one of the storagenodes. After completion of step 908, the process returns to step 900 inwhich the first storage node generates another data block.

In step 910, the first storage node receives one or more data blocksthat were generated by one or more other storage nodes but have hashesfalling within the hash space assigned to the first storage node. Thefirst storage node adds any such data blocks received from other storagenodes to its pool of available data blocks. Each of the other storagenodes is assumed to operate in a similar manner with respect to any datablocks received from the first storage node or another one of thestorage nodes, in that it adds any such received data blocks to its poolof available data blocks.

In step 912, a determination is made as to whether or not the pool ofavailable data blocks maintained by the first storage node contains atleast a threshold number of data blocks. The threshold number of datablocks can be determined based on the particular prefilling context inorder to ensure that desired prefilling targets or levels of “fullness”of the storage devices can be achieved in a highly efficient manner. Ifthe pool includes at least the threshold number of data blocks, theprocess moves to step 914, and otherwise moves to step 916 as indicated.

In step 914, during a prefill cycle in which the storage nodes prefilltheir associated storage nodes using available data blocks from theirrespective pools, the first storage node utilizes data blocks from itspool to prefill its storage devices. The process then moves to step 918.

In step 916, which is reached if the determination in step 912 indicatesthat the pool maintained by the first storage node does not include atleast the threshold number of data blocks, the first storage nodenotifies other storage nodes that additional data blocks are needed bythe first storage node. After completion of step 916, the processreturns to step 900 in which the first storage node generates anotherdata block.

In step 918, a determination is made as to whether or not the currentprefill cycle is complete. Completion of the current prefill cycleillustratively indicates that the storage nodes have prefilled theirassociated storage devices to desired prefilling targets or levels offullness. If the prefill cycle is not complete, the process returns tostep 912 to once again test the sufficiency of the pool of availabledata blocks. If the prefill cycle is complete, the process ends asindicated. One or more additional prefill cycles can be executed throughrepetition of the FIG. 9 process, possibly at certain predeterminedintervals or responsive to occurrence of particular designated events.

As noted above, operations referred to above as being performed by thefirst storage node are similarly performed by other ones of the storagenodes.

Again, the sequential nature of the flow diagram should not be viewed asrequiring that the steps be performed synchronously. Instead,asynchronous performance of these steps is contemplated in manyimplementations. For example, different sets of the steps, such as setscomprising steps {900-906}, {910}, {912-916} and {914-918} can beperformed as respective asynchronous tasks.

In one example implementation of the FIG. 9 process, in conjunction witha given prefill cycle, each of the storage nodes maintains a pool ofdata blocks that are appropriate for itself, as the data blocks of thepool of each storage node have hashes that fall within its correspondingassigned portion of the hash space. When the pool maintained by a givenone of the storage nodes becomes low, that storage node will communicateto all of the other storage nodes that it needs additional data blocks.In this manner, when any of the storage nodes requires additional datablocks, all of the storage nodes will generate data blocks in parallel.When each data block is generated, the appropriate storage node for thatdata block is determined and the data block will either be added to thepool of the generating storage node if that storage node is theappropriate storage node for the data block, or it will be sent to theappropriate storage node and added to the pool maintained by thatstorage node. The pools of data blocks maintained by the storage nodesare drawn on as needed by the prefill task.

These and other example implementations of the FIG. 9 processadvantageously avoid the difficulties that would otherwise be associatedwith each of the storage nodes generating multiple random blocks, anddiscarding inappropriate ones of those random blocks, until a sufficientnumber of random blocks are generated that are appropriate for theparticular generating storage node. Instead, such implementations areconfigured such that every generated data block is utilized by one ofthe storage nodes, while also significantly reducing the total number ofhash computations required.

Illustrative embodiments of storage systems with distributed datageneration and associated RAID array prefilling as disclosed herein cantherefore provide a number of significant advantages relative toarrangements such as independent and separate generation of random databy each storage node to prefill its storage devices, or prefillingstorage devices with random data generated by one or more host devices.

The particular processing operations and other system functionalitydescribed above in conjunction with the flow diagram of FIG. 9 arepresented by way of illustrative example only, and should not beconstrued as limiting the scope of the disclosure in any way.Alternative embodiments can use other types of processing operations forimplementing distributed data generation and associated RAID arrayprefilling in a storage system. For example, as indicated above, theordering of the process steps may be varied in other embodiments, orcertain steps may be performed at least in part concurrently with oneanother rather than serially. Also, one or more of the process steps maybe repeated periodically, or multiple instances of the process can beperformed in parallel with one another in order to support multipleinstances of distributed data generation and associated RAID arrayprefilling for multiple storage systems or different portions of asingle storage system.

Functionality such as that described in conjunction with the flowdiagram of FIG. 9 can be implemented at least in part in the form of oneor more software programs stored in memory and executed by a processorof a processing device such as a computer or server. As will bedescribed below, a memory or other storage device having executableprogram code of one or more software programs embodied therein is anexample of what is more generally referred to herein as a“processor-readable storage medium.”

A storage controller such as distributed storage controller 808 that isconfigured to control performance of one or more steps of the process ofthe flow diagram of FIG. 9 can be implemented as part of what is moregenerally referred to herein as a processing platform comprising one ormore processing devices each comprising a processor coupled to a memory.A given such processing device may correspond to one or more virtualmachines or other types of virtualization infrastructure such as Dockercontainers or Linux containers (LXCs). The host devices 102 or 202 andcontent addressable storage system 805, as well as other systemcomponents, may be implemented at least in part using processing devicesof such processing platforms. For example, in the distributed storagecontroller 808, respective distributed modules can be implemented inrespective containers running on respective ones of the processingdevices of a processing platform.

The FIG. 9 process makes use of various metadata structures that aremaintained within the content addressable storage system 805. Examplesof metadata structures maintained by the storage system in illustrativeembodiments include the logical layer and physical layer mapping tablesshown in respective FIGS. 10A, 10B, 10C and 10D. It is to be appreciatedthat these particular tables are only examples, and other tables ormetadata structures having different configurations of entries andfields can be used in other embodiments.

Referring initially to FIG. 10A, an address-to-hash (“A2H”) table 1000is shown. The A2H table 1000 comprises a plurality of entries accessibleutilizing logical addresses denoted Logical Address 1, Logical Address2, . . . Logical Address M as respective keys, with each such entry ofthe A2H table 1000 comprising a corresponding one of the logicaladdresses, a corresponding one of the hash handles, and possibly one ormore additional fields.

FIG. 10B shows a hash-to-data (“H2D”) table 1002 that illustrativelycomprises a plurality of entries accessible utilizing hash handlesdenoted Hash Handle 1, Hash Handle 2, . . . Hash Handle D as respectivekeys, with each such entry of the H2D table 1002 comprising acorresponding one of the hash handles, a physical offset of acorresponding one of the data pages, and possibly one or more additionalfields.

Referring now to FIG. 10C, a hash metadata (“HMD”) table 1004 comprisesa plurality of entries accessible utilizing hash handles denoted HashHandle 1, Hash Handle 2, . . . Hash Handle H as respective keys. Eachsuch entry of the HMD table 1004 comprises a corresponding one of thehash handles, a corresponding reference count and a correspondingphysical offset of one of the data pages. A given one of the referencecounts denotes the number of logical pages in the storage system thathave the same content as the corresponding data page and therefore pointto that same data page via their common hash digest. Although notexplicitly so indicated in the figure, the HMD table 1004 may alsoinclude one or more additional fields.

In the present embodiment, the HMD table of FIG. 10C illustrativelycomprises at least a portion of the same information that is found inthe H2D table of FIG. 10B. Accordingly, in other embodiments, those twotables can be combined into a single table, illustratively referred toas an H2D table, an HMD table or another type of physical layer mappingtable providing a mapping between hash values, such as hash handles orhash digests, and corresponding physical addresses of data pages.

FIG. 10D shows a physical layer based (“PLB”) table 1006 thatillustratively comprises a plurality of entries accessible utilizingphysical offsets denoted Physical Offset 1, Physical Offset 2, . . .Physical Offset P as respective keys, with each such entry of the PLBtable 1006 comprising a corresponding one of the physical offsets, acorresponding one of the hash digests, and possibly one or moreadditional fields.

As indicated above, the hash handles are generally shorter in lengththan the corresponding hash digests of the respective data pages, andeach illustratively provides a short representation of the correspondingfull hash digest. For example, in some embodiments, the full hashdigests are 20 bytes in length, and their respective corresponding hashhandles are illustratively only 4 or 6 bytes in length.

Collisions can arise where data pages with different content nonethelesshave the same hash handle. This is a possibility in embodiments thatutilize hash handles rather than full hash digests to identify datapages. Unlike the full hash digests which are generated usingcollision-resistant hash functions that can essentially guarantee uniquehash digests for data pages with different content, the hash handles canin some cases with very small probability lead to collisions. The hashhandle lengths and their manner of generation should therefore beselected so as to ensure that the collision probability is at or below amaximum acceptable level for the particular implementation.

It is to be appreciated that terms such as “table” and “entry” as usedherein are intended to be broadly construed, and the particular exampletable and entry arrangements of FIGS. 10A through 10D can be varied inother embodiments. For example, additional or alternative arrangementsof tables and entries can be used.

The content addressable storage system 805 utilizes a two-level mappingprocess to map logical block addresses to physical block addresses. Forexample, the first level of mapping illustratively uses the A2H tableand the second level of mapping uses the HMD table, with the A2H and HMDtables corresponding to respective logical and physical layers of thecontent-based signature mapping within the content addressable storagesystem 805. The HMD table or a given portion thereof in some embodimentsdisclosed herein is more particularly referred to as an H2D table.

The first level of mapping using the A2H table associates logicaladdresses of respective data pages with respective content-basedsignatures of those data pages. This is also referred to as logicallayer mapping.

The second level of mapping using the HMD table associates respectiveones of the content-based signatures with respective physical storagelocations in one or more of the storage devices 106. This is alsoreferred to as physical layer mapping.

Examples of these and other metadata structures utilized in illustrativeembodiments include the A2H, H2H, HMD and PLB tables of respective FIGS.10A through 10D. In some embodiments, the A2H and H2D tables areutilized primarily by the control modules 808C, while the HDM and PLBtables are utilized primarily by the data modules 808D.

For a given write request, hash metadata comprising at least a subset ofthe above-noted tables is updated in conjunction with the processing ofthat write request.

The A2H, H2H, HMD and PLB tables described above are examples of whatare more generally referred to herein as “mapping tables” of respectivedistinct types. Other types and arrangements of mapping tables or othercontent-based signature mapping information may be used in otherembodiments.

Such mapping tables are still more generally referred to herein as“metadata structures” of the content addressable storage system 805. Itshould be noted that additional or alternative metadata structures canbe used in other embodiments. References herein to particular tables ofparticular types, such as A2H, H2D, MMD and PLB tables, and theirrespective configurations, should be considered non-limiting and arepresented by way of illustrative example only. Such metadata structurescan be implemented in numerous alternative configurations with differentarrangements of fields and entries in other embodiments.

The logical block addresses or LBAs of a logical layer of the storagesystem 805 correspond to respective physical blocks of a physical layerof the storage system 805. The user data pages of the logical layer areorganized by LBA and have reference via respective content-basedsignatures to particular physical blocks of the physical layer.

Each of the physical blocks has an associated reference count that ismaintained within the storage system 805. The reference count for agiven physical block indicates the number of logical blocks that pointto that same physical block.

In releasing logical address space in the storage system, adereferencing operation is generally executed for each of the LBAs beingreleased. More particularly, the reference count of the correspondingphysical block is decremented. A reference count of zero indicates thatthere are no longer any logical blocks that reference the correspondingphysical block, and so that physical block can be released.

It should also be understood that the particular arrangement of storagecontroller processing modules 808C, 808D, 808R and 808M as shown in theFIG. 8 embodiment is presented by way of example only. Numerousalternative arrangements of processing modules of a distributed storagecontroller may be used to implement functionality for distributedgeneration of data in a clustered storage system in other embodiments.

Additional examples of content addressable storage functionalityimplemented in some embodiments by control modules 808C, data modules808D, routing modules 808R and management module(s) 808M of distributedstorage controller 808 can be found in U.S. Pat. No. 9,104,326, entitled“Scalable Block Data Storage Using Content Addressing,” which isincorporated by reference herein. Alternative arrangements of these andother storage node processing modules of a distributed storagecontroller in a content addressable storage system can be used in otherembodiments.

A second group of additional illustrative embodiments provides automaticprefill of a storage array or other type of storage system withconditioning of RAID stripes.

In a given embodiment of this type, a number of volumes with a size anddesired fill percentage is specified. From that specification, acalculation is made of the total physical space that will be in use oncethose volumes are created. From this, a determination is made of thenumber of blocks that should be used or free within each RAID stripe.

An automatic process will then process each stripe. For each stripe itwill fill the appropriate number of free blocks and put them into thestripe parity calculation. It will also assure that the remaining freeblocks are included in the parity calculation as well. The prefilledblocks of each stripe are now placed on a list of available blocks.

For each specified volume, a separate automatic process will then makerequests for random data to fill each volume to the specifiedpercentage. When requesting the random data, it will consume theprefilled blocks that were created from the stripe prefill process.

An embodiment of this type can be implemented, for example, in thecontent addressable storage system 805 of FIG. 8 utilizing its storagenodes 815 each comprising one or more of the storage devices 806. Thestorage system 805 in such an embodiment is configured to prefill agiven stripe across multiple ones of the storage devices 806 with datablocks to a first fullness level, to designate at least a subset of theprefilled data blocks of the stripe as available for use in prefillingone or more logical storage volumes of the storage system 805, and toprefill a given one of the logical storage volumes to a second fullnesslevel utilizing selected ones of the available data blocks.

The given stripe across multiple ones of the storage devicesillustratively comprises a RAID stripe of the type as describedelsewhere herein, although other types of striping arrangements can beused.

In some embodiments, the first and second fullness levels are the same,although different first and second fullness levels can be used in otherembodiments. At least one of the first and second fullness levels isillustratively specified as a percentage fullness, although otherfullness measures can be used.

In some embodiments, the storage system 805 is further configured todetermine sizes of the one or more logical storage volumes, and tocompute the first fullness level based at least in part on thedetermined sizes of the one or more logical storage volumes.

Additionally or alternatively, the storage system 805 in someembodiments is illustratively configured to determine from the firstfullness level a number of free data blocks to be included within thegiven stripe, and to prefill the given stripe based at least in part onthe determined number of free data blocks.

The prefilling of the given stripe in illustrative embodiments isconfigured to ensure that each of the free data blocks has been writtento at least one time. The storage system 805 is further configured toinclude each of the prefilled data blocks of the given stripe in atleast one parity computation. As indicated above, the prefilling of thegiven stripe and the prefilling of the given logical storage volume insome embodiments are implemented as respective separate automaticprocesses.

Such arrangements advantageously avoid the difficulties that wouldotherwise be associated with writing and overwriting each in-use datablock from a front-end host or hosts until every block in the storagearray has been written to at least once, leaving no blocks that havenever been written to.

A third group of additional illustrative embodiments provides prefill ofRAID stripes in a storage array or other type of storage system byreading of existing data.

Reading of data from storage drives generally occurs much faster thanwriting of the drives. For each RAID stripe, we read the blocks that aredesired to be filled. Based on the content of each block, we candetermine which node this block is designated for. After the appropriatenode is determined and if the block is appropriate for this node, wemark the block as used and include it in parity calculation. If theblock is not appropriate for this node, we transmit the block to theappropriate node. This results in significant performance increase dueto the fact that transmitting a block to another node is faster thangenerating a new random block that meets the criteria for this node.Each node maintains a queue of blocks received from other nodes, and candraw on this queue to when it needs a random block.

An embodiment of this type can be implemented, for example, in thecontent addressable storage system 805 of FIG. 8 utilizing its storagenodes 815 each comprising one or more of the storage devices 806. Atleast a given one of the storage nodes 815 is configured to read datablocks from its one or more storage devices, and for a given one of thedata blocks, to determine based at least in part on a content-basedsignature of that data block whether or not the given data block isappropriate for use in a prefilling operation of the given storage node.Responsive to the given data block being appropriate for use in theprefilling operation of the given storage node, the given storage nodeuses the data block in the prefilling operation of the given storagenode, and responsive to the given data block not being appropriate foruse in the prefilling operation of the given storage node, the givenstorage node sends the data block to another one of the storage nodesfor use in a prefilling operation of that other storage node.

Also by way of example, embodiments of the type described above can beimplemented using a process similar to that of FIG. 9, but with thefirst storage node reading the data blocks from its one or more storagedevices, rather than generating those data blocks using a PRNG or othertype of data block generation circuitry. References herein to“distributed generation of data” should be understood to encompassarrangements that generate data in a distributed manner by reading datablocks from storage devices.

In some embodiments, the given storage node in reading data blocks fromits one or more storage devices is further configured to read the datablocks from at least one designated RAID stripe. Other embodiments canutilize other techniques for reading data blocks from storage devices.

The given storage node in some embodiments is further configured tocompute content-based signatures for the respective data blocks. For atleast one of the data blocks having a content-based signature that iswithin a portion of a content-based signature space assigned to thegiven storage node, the given storage node designates the data block asappropriate for use in the prefilling operation of the given storagenode. For at least one of the data blocks having a content-basedsignature that is not within a portion of a content-based signaturespace assigned to the given storage node, the given storage nodedesignates the data block as not appropriate for use in the prefillingoperation of the given storage node.

In some embodiments, the given storage node is further configured tosend the data block to another one of the storage nodes for which thecontent-based signature for the data block is within a portion of thecontent-based signature space assigned to that other storage node.

For a particular one of the data blocks that is appropriate for use inthe prefilling operation of the given storage node, the given storagenode is illustratively configured to use the particular data block inthe prefilling operation, to mark the particular data block as used, andto include the particular data block in a parity computation, such as aparity computation of the type described elsewhere herein.

In some embodiments, the given storage node is further configured tomaintain a pool of data blocks received from other ones of the storagenodes and determined by those other storage nodes to be appropriate foruse in the prefilling operation of the given storage node. Each of theother storage nodes also maintains a pool of data blocks received fromother ones of the storage nodes.

The content-based signatures are illustratively part of a designatedcontent-based signature space of the storage system 805, with differentportions of the content-based signature space of the storage system 805being assigned to respective different ones of the storage nodes 815. Asindicated previously, the content-based signature space of the storagesystem 805 illustratively comprises one of a hash handle space of thestorage system and a hash digest space of the storage system 805.

Such arrangements advantageously avoid the difficulties that wouldotherwise be associated with the given storage node having to provideall of the data blocks for prefilling that storage node, or requiringthat all of the data blocks for prefilling the given storage node bewritten to that storage node from one or more front-end hosts.

The above-described first, second and third groups of embodiments can beutilized for prefilling of storage arrays or other types of storagesystems for purposes of performance testing, quality assurance testingor other types of testing, as well as for other purposes, and can beimplemented in automated tools deployed within the storage array forsuch purposes. It is generally desirable to test the storage array underspecified conditions of sufficient “fullness” of the storage array, andthese embodiments greatly facilitate the establishment of suchconditions. As indicated previously, random data referred to in theseand other embodiments herein can be generated at least in part usingPRNGs or other circuitry or techniques for producing random orpseudorandom data. Other embodiments need not utilize random data, butcan instead utilize other types of data, such as existing data read fromstorage devices.

The above-described embodiments can be implemented in storage systems ofthe type described elsewhere herein, and accordingly are not limited touse in content addressable storage systems. Again, a wide variety ofdifferent RAID and parity computation arrangements can be used.

In some embodiments, the storage system comprises an XtremIO™ storagearray or other type of content addressable storage system suitablymodified to incorporate functionality for distributed data generationand associated prefilling of RAID stripes as disclosed herein.

It is to be appreciated that the particular advantages described aboveand elsewhere herein are associated with particular illustrativeembodiments and need not be present in other embodiments. Also, theparticular types of information processing system features andfunctionality as illustrated in the drawings and described above areexemplary only, and numerous other arrangements may be used in otherembodiments.

Illustrative embodiments of processing platforms utilized to implementhost devices and storage systems with functionality for parity datacomputation will now be described in greater detail with reference toFIGS. 11 and 12. Although described in the context of system 100, theseplatforms may also be used to implement at least portions of otherinformation processing systems in other embodiments.

FIG. 11 shows an example processing platform comprising cloudinfrastructure 1100. The cloud infrastructure 1100 comprises acombination of physical and virtual processing resources that may beutilized to implement at least a portion of the information processingsystem 100. The cloud infrastructure 1100 comprises multiple virtualmachines (VMs) and/or container sets 1102-1, 1102-2, . . . 102-Limplemented using virtualization infrastructure 1104. The virtualizationinfrastructure 1104 runs on physical infrastructure 1105, andillustratively comprises one or more hypervisors and/or operating systemlevel virtualization infrastructure. The operating system levelvirtualization infrastructure illustratively comprises kernel controlgroups of a Linux operating system or other type of operating system.

The cloud infrastructure 1100 further comprises sets of applications1110-1, 1110-2, . . . 1110-L running on respective ones of theVMs/container sets 1102-1, 1102-2, . . . 1102-L under the control of thevirtualization infrastructure 1104. The VMs/container sets 1102 maycomprise respective VMs, respective sets of one or more containers, orrespective sets of one or more containers running in VMs.

In some implementations of the FIG. 11 embodiment, the VMs/containersets 1102 comprise respective VMs implemented using virtualizationinfrastructure 1104 that comprises at least one hypervisor. Suchimplementations can provide functionality for distributed datageneration and associated RAID array prefilling using processes runningon one or more of the VMs. For example, each of the VMs can implementsuch functionality using one or more processes running on thatparticular VW.

An example of a hypervisor platform that may be used to implement ahypervisor within the virtualization infrastructure 1104 is the VMware®vSphere® which may have an associated virtual infrastructure managementsystem such as the VMware® vCenter™. The underlying physical machinesmay comprise one or more distributed processing platforms that includeone or more storage systems.

In other implementations of the FIG. 11 embodiment, the VMs/containersets 1102 comprise respective containers implemented usingvirtualization infrastructure 1104 that provides operating system levelvirtualization functionality, such as support for Docker containersrunning on bare metal hosts, or Docker containers running on VMs. Thecontainers are illustratively implemented using respective kernelcontrol groups of the operating system. Such implementations can providefunctionality for distributed data generation and associated RAID arrayprefilling using processes running on different ones of the containers.For example, a container host device supporting multiple containers ofone or more container sets can implement one or more instances of suchfunctionality.

As is apparent from the above, one or more of the processing modules orother components of system 100 may each run on a computer, server,storage device or other processing platform element. A given suchelement may be viewed as an example of what is more generally referredto herein as a “processing device.” The cloud infrastructure 1100 shownin FIG. 11 may represent at least a portion of one processing platform.Another example of such a processing platform is processing platform1200 shown in FIG. 12.

The processing platform 1200 in this embodiment comprises a portion ofsystem 100 or 200 and includes a plurality of processing devices,denoted 1202-1, 1202-2, 1202-3, . . . 1202-K, which communicate with oneanother over a network 1204.

The network 1204 may comprise any type of network, including by way ofexample a global computer network such as the Internet, a WAN, a LAN, asatellite network, a telephone or cable network, a cellular network, awireless network such as a WiFi or WiMAX network, or various portions orcombinations of these and other types of networks.

The processing device 1202-1 in the processing platform 1200 comprises aprocessor 1210 coupled to a memory 1212.

The processor 1210 may comprise a microprocessor, a microcontroller, anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA) or other type of processing circuitry, as well asportions or combinations of such circuitry elements.

The memory 1212 may comprise random access memory (RA), read-only memory(ROM), flash memory or other types of memory, in any combination. Thememory 1212 and other memories disclosed herein should be viewed asillustrative examples of what are more generally referred to as“processor-readable storage media” storing executable program code ofone or more software programs.

Articles of manufacture comprising such processor-readable storage mediaare considered illustrative embodiments. A given such article ofmanufacture may comprise, for example, a storage array, a storage diskor an integrated circuit containing RAM, ROM, flash memory or otherelectronic memory, or any of a wide variety of other types of computerprogram products. The term “article of manufacture” as used hereinshould be understood to exclude transitory, propagating signals.Numerous other types of computer program products comprisingprocessor-readable storage media can be used.

Also included in the processing device 1202-1 is network interfacecircuitry 1214, which is used to interface the processing device withthe network 1204 and other system components and may compriseconventional transceivers.

The other processing devices 1202 of the processing platform 1200 areassumed to be configured in a manner similar to that shown forprocessing device 1202-1 in the figure.

Again, the particular processing platform 1200 shown in the figure ispresented by way of example only, and system 100 or 200 may includeadditional or alternative processing platforms, as well as numerousdistinct processing platforms in any combination, with each suchplatform comprising one or more computers, servers, storage devices orother processing devices.

For example, other processing platforms used to implement illustrativeembodiments can comprise converged infrastructure such as VxRail™,VxRack™, VxRack™ FLEX, VxBlock™, or Vblock® converged infrastructurefrom Dell EMC.

It should therefore be understood that in other embodiments differentarrangements of additional or alternative elements may be used. At leasta subset of these elements may be collectively implemented on a commonprocessing platform, or each such element may be implemented on aseparate processing platform.

As indicated previously, components of an information processing systemas disclosed herein can be implemented at least in part in the form ofone or more software programs stored in memory and executed by aprocessor of a processing device. For example, at least portions of thefunctionality for distributed data generation and associated RAID arrayprefilling as disclosed herein are illustratively implemented in theform of software running on one or more processing devices.

It should again be emphasized that the above-described embodiments arepresented for purposes of illustration only. Many variations and otheralternative embodiments may be used. For example, the disclosedtechniques are applicable to a wide variety of other types ofinformation processing systems, host devices, storage systems, storagenodes, storage devices, storage controllers, RAID arrangements, andtechniques for distributed data generation and associated RAID arrayprefilling. Also, the particular configurations of system and deviceelements and associated processing operations illustratively shown inthe drawings can be varied in other embodiments. Moreover, the variousassumptions made above in the course of describing the illustrativeembodiments should also be viewed as exemplary rather than asrequirements or limitations of the disclosure. Numerous otheralternative embodiments within the scope of the appended claims will bereadily apparent to those skilled in the art.

What is claimed is:
 1. An apparatus comprising: a storage systemcomprising a plurality of storage nodes each comprising one or morestorage devices; each of the storage nodes further comprising aprocessor coupled to a memory; at least a given one of the storage nodesbeing configured: to read data blocks from its one or more storagedevices; for a given one of the data blocks, to determine based at leastin part on a content-based signature of that data block whether or notthe given data block is appropriate for use in a prefilling operation ofthe given storage node; responsive to the given data block beingappropriate for use in the prefilling operation of the given storagenode, to use the data block in the prefilling operation of the givenstorage node; and responsive to the given data block not beingappropriate for use in the prefilling operation of the given storagenode, to send the data block to another one of the storage nodes for usein a prefilling operation of that other storage node.
 2. The apparatusof claim 1 wherein the storage system is configured to store the datablocks across the storage devices of the storage nodes utilizing aredundant array of independent disks (RAID) arrangement.
 3. Theapparatus of claim 2 wherein the RAID arrangement includes parityinformation supporting at least one recovery option for reconstructingthe data blocks of at least one of the storage devices responsive to afailure of that storage device.
 4. The apparatus of claim 1 wherein eachof the storage nodes further comprises a set of processing modulesconfigured to communicate over one or more networks with correspondingsets of processing modules on other ones of the storage nodes.
 5. Theapparatus of claim 4 wherein the sets of processing modules compriserespective servers that collectively implement at least a portion of adistributed storage controller of the storage system.
 6. The apparatusof claim 4 wherein the sets of processing modules of the storage nodeseach comprise at least one data module and at least one control module.7. The apparatus of claim 1 wherein the given storage node in readingdata blocks from its one or more storage devices is further configuredto read the data blocks from at least one designated RAID stripe.
 8. Theapparatus of claim 1 wherein the given storage node is furtherconfigured: to compute content-based signatures for the respective datablocks; for at least one of the data blocks having a content-basedsignature that is within a portion of a content-based signature spaceassigned to the given storage node, to designate the data block asappropriate for use in the prefilling operation of the given storagenode; and for at least one of the data blocks having a content-basedsignature that is not within a portion of a content-based signaturespace assigned to the given storage node, to designate the data block asnot appropriate for use in the prefilling operation of the given storagenode.
 9. The apparatus of claim 8 wherein the given storage node isfurther configured to send the data block to another one of the storagenodes for which the content-based signature for the data block is withina portion of the content-based signature space assigned to that otherstorage node.
 10. The apparatus of claim 1 wherein the given storagenode is further configured, for a particular one of the data blocks thatis appropriate for use in the prefilling operation of the given storagenode: to use the particular data block in the prefilling operation; tomark the particular data block as used; and to include the particulardata block in a parity computation.
 11. The apparatus of claim 1 whereinthe given storage node is further configured to maintain a pool of datablocks received from other ones of the storage nodes and determined bythose other storage nodes to be appropriate for use in the prefillingoperation of the given storage node.
 12. The apparatus of claim 11wherein each of the other storage nodes also maintains a pool of datablocks received from other ones of the storage nodes.
 13. The apparatusof claim 1 wherein the content-based signatures are part of a designatedcontent-based signature space of the storage system and whereindifferent portions of the content-based signature space of the storagesystem are assigned to respective different ones of the storage nodes.14. The apparatus of claim 13 wherein the content-based signature spaceof the storage system comprises one of a hash handle space of thestorage system and a hash digest space of the storage system.
 15. Amethod comprising: configuring a storage system to include a pluralityof storage nodes each comprising one or more storage devices, each ofthe storage nodes further comprising a processor coupled to a memory;reading data blocks from the one or more storage devices of a given oneof the storage nodes; for a given one of the data blocks, determiningbased at least in part on a content-based signature of that data blockwhether or not the given data block is appropriate for use in aprefilling operation of the given storage node; responsive to the givendata block being appropriate for use in the prefilling operation of thegiven storage node, using the data block in the prefilling operation ofthe given storage node; and responsive to the given data block not beingappropriate for use in the prefilling operation of the given storagenode, sending the data block to another one of the storage nodes for usein a prefilling operation of that other storage node.
 16. The method ofclaim 15 further comprising: computing content-based signatures for therespective data blocks; for at least one of the data blocks having acontent-based signature that is within a portion of a content-basedsignature space assigned to the given storage node, designating the datablock as appropriate for use in the prefilling operation of the givenstorage node; and for at least one of the data blocks having acontent-based signature that is not within a portion of a content-basedsignature space assigned to the given storage node, designating the datablock as not appropriate for use in the prefilling operation of thegiven storage node.
 17. The method of claim 15 further comprising, for aparticular one of the data blocks that is appropriate for use in theprefilling operation of the given storage node: using the particulardata block in the prefilling operation of the given storage node;marking the particular data block as used; and including the particulardata block in a parity computation.
 18. A computer program productcomprising a non-transitory processor-readable storage medium havingstored therein program code of one or more software programs, whereinthe program code when executed by a storage system comprising aplurality of storage nodes each comprising one or more storage devices,each of the storage nodes further comprising a processor coupled to amemory, causes the storage system: to read data blocks from the one ormore storage devices of a given one of the storage nodes; for a givenone of the data blocks, to determine based at least in part on acontent-based signature of that data block whether or not the given datablock is appropriate for use in a prefilling operation of the givenstorage node; responsive to the given data block being appropriate foruse in the prefilling operation of the given storage node, to use thedata block in the prefilling operation of the given storage node; andresponsive to the given data block not being appropriate for use in theprefilling operation of the given storage node, to send the data blockto another one of the storage nodes for use in a prefilling operation ofthat other storage node.
 19. The computer program product of claim 18wherein the program code when executed by the storage system furthercauses the storage system: to compute content-based signatures for therespective data blocks; for at least one of the data blocks having acontent-based signature that is within a portion of a content-basedsignature space assigned to the given storage node, to designate thedata block as appropriate for use in the prefilling operation of thegiven storage node; and for at least one of the data blocks having acontent-based signature that is not within a portion of a content-basedsignature space assigned to the given storage node, to designate thedata block as not appropriate for use in the prefilling operation of thegiven storage node.
 20. The computer program product of claim 18 whereinthe program code when executed by the storage system further causes thestorage system, for a particular one of the data blocks that isappropriate for use in the prefilling operation of the given storagenode: to use the particular data block in the prefilling operation ofthe given storage node; to mark the particular data block as used; andto include the particular data block in a parity computation.